Dr Holly-May Lewis

Senior Laboratory Technician UPLC-MS (Chronobiology)


Holly-May Lewis, Priyanka Gupta, Kyle DG Saunders, Shazneil Briones, Johanna Von Gerichten, Paul A Townsend, Eirini Velliou, Dany Beste, Olivier Cexus, Roger Paul Webb, Melanie Jane Bailey (2023)Nanocapillary sampling coupled to liquid chromatography mass spectrometry delivers single cell drug measurement and lipid fingerprints, In: Analyst148(5)pp. 1041-1049 Royal Society of Chemistry

This work describes the development of a new approach to measure drug levels and lipid fingerprints in single living mammalian cells. Nanocapillary sampling is an approach that enables the selection and isolation of single living cells under microscope observation. Here, live single cell nanocapillary sampling is coupled to liquid chromatography for the first time. This allows molecular species to be separated prior to ionisation and improves measurement precision of drug analytes. The efficiency of transferring analytes from the sampling capillary into a vial was optimised in this work. The analysis was carried out using standard flow liquid chromatography coupled to widely available mass spectrometry instrumentation, highlighting opportunities for widespread adoption. The method was applied to 30 living cells, revealing cell-to-cell heterogeneity in the uptake of different antibiotics. Using this system, we detected 14-158 lipid features per single cell, revealing the association between bedaquiline uptake and lipid fingerprints.

Imesha De Silva, Amanda R Kretsch, Holly-May Lewis, Melanie Bailey, Guido Fridolin Verbeck (2019)True One Cell Chemical Analysis: A Review, In: The Analyst144pp. 4733-4749 Royal Society of Chemistry

The constantly growing field of the true one cell analysis provides important information on the direct chemical composition of various cells and cellular compartments. Since the heterogeneity of individual cells has been established, more researchers are interested in the chemical differences between individual cells and that is the only analysis of the one cell can determine. This results in new technologies and methods being reported regularly. This review highlights the common techniques of micro- and nanomanipulation, Raman spectroscopy, microsocopy, and mass spectrometric imaging as they pertain to the true one cell chemical analysis.

Holly-May Lewis, Roger Webb, Guido F Verbeck, Josephine Bunch, Janella De Jesus, Catia Costa, Vladimir Palitsin, John G. Swales, Richard J. A. Goodwin, Patrick Sears, Melanie Jane Bailey (2019)Nanoextraction coupled to liquid chromatography mass spectrometry delivers improved spatially resolved analysis, In: Analytical Chemistry91(24)pp. 15411-15417 American Chemical Society

Direct analyte probed nanoextraction (DAPNe) is a technique that allows extraction of drug and endogenous compounds from a discrete location on a tissue sample using a nano capillary filled with solvent. Samples can be extracted from a spot diameters as low as 6 µm. Studies previously undertaken by our group have shown that the technique can provide good precision (5%) for analysing drug molecules in 150 µm diameter areas of homogenised tissue, provided an internal standard is sprayed on to the tissue prior to analysis. However, without an isotopically labelled standard, the repeatability is poor, even after normalisation to and the spot area or matrix compounds. By application to tissue homogenates spiked with drug compounds, we can demonstrate that it is possible to significantly improve the repeatability of the technique by incorporating a liquid chromatography separation step. Liquid chromatography is a technique for separating compounds prior to mass spectrometry (LC-MS) which enables separation of isomeric compounds that cannot be discriminated using mass spectrometry alone, as well as reducing matrix interferences. Conventionally, LC-MS is carried out on bulk or homogenised samples, which means analysis is essentially an average of the sample and does not take into account discrete areas. This work opens a new opportunity for spatially resolved liquid chromatography mass spectrometry with precision better than 20%.

Holly-May Lewis, Catia Costa, Véronique Dartois, Firat Kaya, Mark Chambers, Janella de Jesus, Vladimir Palitsin, Roger Webb, Melanie J. Bailey (2022)Colocation of Lipids, Drugs, and Metal Biomarkers Using Spatially Resolved Lipidomics with Elemental Mapping, In: Analytical chemistry (Washington)94(34)pp. 11798-11806 American Chemical Society

Elemental imaging is widely used for imaging cells and tissues but rarely in combination with organic mass spectrometry, which can be used to profile lipids and measure drug concentrations. Here, we demonstrate how elemental imaging and a new method for spatially resolved lipidomics (DAPNe-LC-MS, based on capillary microsampling and liquid chromatography mass spectrometry) can be used in combination to probe the relationship between metals, drugs, and lipids in discrete areas of tissues. This new method for spatial lipidomics, reported here for the first time, has been applied to rabbit lung tissues containing a lesion (caseous granuloma) caused by tuberculosis infection. We demonstrate how elemental imaging with spatially resolved lipidomics can be used to probe the association between ion accumulation and lipid profiles and verify local drug distribution.

Cecile F. Frampas, Katie Longman, Matt Spick, Holly-May Lewis, Catia D. S. Costa, Alex Stewart, Deborah Dunn-Walters, Danni Greener, George Evetts, Debra J. Skene, Drupad Trivedi, Andy Pitt, Katherine Hollywood, Perdita Barran, Melanie J. Bailey (2022)Untargeted saliva metabolomics by liquid chromatography-Mass spectrometry reveals markers of COVID-19 severity, In: PloS one17(9)pp. e0274967-e0274967 Public Library Science

Background The COVID-19 pandemic is likely to represent an ongoing global health issue given the potential for new variants, vaccine escape and the low likelihood of eliminating all reservoirs of the disease. Whilst diagnostic testing has progressed at a fast pace, the metabolic drivers of outcomes-and whether markers can be found in different biofluids-are not well understood. Recent research has shown that serum metabolomics has potential for prognosis of disease progression. In a hospital setting, collection of saliva samples is more convenient for both staff and patients, and therefore offers an alternative sampling matrix to serum. Methods Saliva samples were collected from hospitalised patients with clinical suspicion of COVID-19, alongside clinical metadata. COVID-19 diagnosis was confirmed using RT-PCR testing, and COVID-19 severity was classified using clinical descriptors (respiratory rate, peripheral oxygen saturation score and C-reactive protein levels). Metabolites were extracted and analysed using high resolution liquid chromatography-mass spectrometry, and the resulting peak area matrix was analysed using multivariate techniques. Results Positive percent agreement of 1.00 between a partial least squares-discriminant analysis metabolomics model employing a panel of 6 features (5 of which were amino acids, one that could be identified by formula only) and the clinical diagnosis of COVID-19 severity was achieved. The negative percent agreement with the clinical severity diagnosis was also 1.00, leading to an area under receiver operating characteristics curve of 1.00 for the panel of features identified. Conclusions In this exploratory work, we found that saliva metabolomics and in particular amino acids can be capable of separating high severity COVID-19 patients from low severity COVID-19 patients. This expands the atlas of COVID-19 metabolic dysregulation and could in future offer the basis of a quick and non-invasive means of sampling patients, intended to supplement existing clinical tests, with the goal of offering timely treatment to patients with potentially poor outcomes.

Kyle David George Saunders, HOLLY-MAY LEWIS, Dany Beste, Olivier Cexus, Melanie Jane Bailey (2023)Spatial single cell metabolomics: Current challenges and future developments, In: Current opinion in chemical biology75102327 Elsevier Ltd

Single cell metabolomics is a rapidly advancing field of bio-analytical chemistry which aims to observe cellular biology with the greatest detail possible. Mass spectrometry imaging and selective cell sampling (e.g. using nanocapillaries) are two common approaches within the field. Recent achievements such as observation of cell-cell interactions, lipids determining cell states and rapid phenotypic identification demonstrate the efficacy of these approaches and the momentum of the field. However, single cell metabolomics can only continue with the same impetus if the universal challenges to the field are met, such as the lack of strategies for standardisation and quantification, and lack of specificity/sensitivity. Mass spectrometry imaging and selective cell sampling come with unique advantages and challenges which, in many cases are complementary to each other. We propose here that the challenges specific to each approach could be ameliorated with collaboration between the two communities driving these approaches.

Matt Spick, Holly M. Lewis , Michael J. Wilde , Christopher Hopley, Jim Huggett , Melanie J. Bailey (2021)Systematic review with meta-analysis of diagnostic test accuracy for COVID-19 by mass spectrometry, In: Metabolism Elsevier

Background The global COVID-19 pandemic has led to extensive development in many fields, including the diagnosis of COVID-19 infection by mass spectrometry. The aim of this systematic review and meta-analysis was to assess the accuracy of mass spectrometry diagnostic tests developed so far, across a wide range of biological matrices, and additionally to assess risks of bias and applicability in studies published to date. Method 23 retrospective observational cohort studies were included in the systematic review using the PRISMA-DTA framework, with a total of 2 858 COVID-19 positive participants and 2 544 controls. Risks of bias and applicability were assessed via a QUADAS-2 questionnaire. A meta-analysis was also performed focusing on sensitivity, specificity, diagnostic accuracy and Youden’s Index, in addition to assessing heterogeneity. Findings Sensitivity averaged 0.87 in the studies reviewed herein (interquartile range 0.81 - 0.96) and specificity 0.88 (interquartile range 0.82 - 0.98), with an area under the receiver operating characteristic summary curve of 0.93. By subgroup, the best diagnostic results were achieved by viral proteomic analyses of nasopharyngeal swabs and metabolomic analyses of plasma and serum. The performance of other sampling matrices (breath, sebum, saliva) was less good, indicating that these protocols are currently insufficiently mature for clinical application. Conclusions This systematic review and meta-analysis demonstrates the potential for mass spectrometry and ‘omics in achieving accurate test results for COVID-19 diagnosis, but also highlights the need for further work to optimize and harmonize practice across laboratories before these methods can be translated to clinical applications.

Matt Spick, Holly-May Lewis, Cecile F. Frampas, Katie Longman, Catia Costa, Alexander Stewart, Deborah Dunn-Walters, Danni Greener, George Evetts, Michael J. Wilde, Eleanor Sinclair, Perdita E. Barran, Debra J. Skene, Melanie J. Bailey (2022)An integrated analysis and comparison of serum, saliva and sebum for COVID-19 metabolomics, In: Scientific reports1211867 Nature Portfolio

Abstract The majority of metabolomics studies to date have utilised blood serum or plasma, biofluids that do not necessarily address the full range of patient pathologies. Here, correlations between serum metabolites, salivary metabolites and sebum lipids are studied for the first time. 83 COVID-19 positive and negative hospitalised participants provided blood serum alongside saliva and sebum samples for analysis by liquid chromatography mass spectrometry. Widespread alterations to serum-sebum lipid relationships were observed in COVID-19 positive participants versus negative controls. There was also a marked correlation between sebum lipids and the immunostimulatory hormone dehydroepiandrosterone sulphate in the COVID-19 positive cohort. The biofluids analysed herein were also compared in terms of their ability to differentiate COVID-19 positive participants from controls; serum performed best by multivariate analysis (sensitivity and specificity of 0.97), with the dominant changes in triglyceride and bile acid levels, concordant with other studies identifying dyslipidemia as a hallmark of COVID-19 infection. Sebum performed well (sensitivity 0.92; specificity 0.84), with saliva performing worst (sensitivity 0.78; specificity 0.83). These findings show that alterations to skin lipid profiles coincide with dyslipidaemia in serum. The work also signposts the potential for integrated biofluid analyses to provide insight into the whole-body atlas of pathophysiological conditions.

Holly-May Lewis, Yufan Liu, Cecile F. Frampas, Katie Longman, Matt Spick, Alexander Stewart, Emma Sinclair, Nora Kasar, Danni Greener, Anthony D. Whetton, Perdita E. Barran, Tao Chen, Deborah Dunn-Walters, Debra J. Skene, Melanie J. Bailey (2022)Metabolomics Markers of COVID-19 Are Dependent on Collection Wave, In: Metabolites12(8)713 MDPI AG

The effect of COVID-19 infection on the human metabolome has been widely reported, but to date all such studies have focused on a single wave of infection. COVID-19 has generated numerous waves of disease with different clinical presentations, and therefore it is pertinent to explore whether metabolic disturbance changes accordingly, to gain a better understanding of its impact on host metabolism and enable better treatments. This work used a targeted metabolomics platform (Biocrates Life Sciences) to analyze the serum of 164 hospitalized patients, 123 with confirmed positive COVID-19 RT-PCR tests and 41 providing negative tests, across two waves of infection. Seven COVID-19-positive patients also provided longitudinal samples 2–7 months after infection. Changes to metabolites and lipids between positive and negative patients were found to be dependent on collection wave. A machine learning model identified six metabolites that were robust in diagnosing positive patients across both waves of infection: TG (22:1_32:5), TG (18:0_36:3), glutamic acid (Glu), glycolithocholic acid (GLCA), aspartic acid (Asp) and methionine sulfoxide (Met-SO), with an accuracy of 91%. Although some metabolites (TG (18:0_36:3) and Asp) returned to normal after infection, glutamic acid was still dysregulated in the longitudinal samples. This work demonstrates, for the first time, that metabolic dysregulation has partially changed over the course of the pandemic, reflecting changes in variants, clinical presentation and treatment regimes. It also shows that some metabolic changes are robust across waves, and these can differentiate COVID-19-positive individuals from controls in a hospital setting. This research also supports the hypothesis that some metabolic pathways are disrupted several months after COVID-19 infection.

MATTHEW PAUL SPICK, Amy Campbell, Ivona Baricevic-Jones, JOHANNA VON GERICHTEN, HOLLY-MAY LEWIS, CECILE FRANCE FRAMPAS, Katie Longman, ALEXANDER STEWART, DEBORAH DUNN-WALTERS, DEBRA JEAN SKENE, NOPHAR GEIFMAN, Anthony D. Whetton, Melanie J. Bailey (2022)Multi-Omics Reveals Mechanisms of Partial Modulation of COVID-19 Dysregulation by Glucocorticoid Treatment, In: International journal of molecular sciences23(20)12079 MDPI

Treatments for COVID-19 infections have improved dramatically since the beginning of the pandemic, and glucocorticoids have been a key tool in improving mortality rates. The UK’s National Institute for Health and Care Excellence guidance is for treatment to be targeted only at those requiring oxygen supplementation, however, and the interactions between glucocorticoids and COVID-19 are not completely understood. In this work, a multi-omic analysis of 98 inpatient-recruited participants was performed by quantitative metabolomics (using targeted liquid chromatography-mass spectrometry) and data-independent acquisition proteomics. Both ‘omics datasets were analysed for statistically significant features and pathways differentiating participants whose treatment regimens did or did not include glucocorticoids. Metabolomic differences in glucocorticoid-treated patients included the modulation of cortisol and bile acid concentrations in serum, but no alleviation of serum dyslipidemia or increased amino acid concentrations (including tyrosine and arginine) in the glucocorticoid-treated cohort relative to the untreated cohort. Proteomic pathway analysis indicated neutrophil and platelet degranulation as influenced by glucocorticoid treatment. These results are in keeping with the key role of platelet-associated pathways and neutrophils in COVID-19 pathogenesis and provide opportunity for further understanding of glucocorticoid action. The findings also, however, highlight that glucocorticoids are not fully effective across the wide range of ‘omics dysregulation caused by COVID-19 infections.